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A Survey on Fatigue Detection of Workers Using Machine Learning

Nisha Yadav, Kakoli Banerjee, Vikram Bali

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)11(3)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799806912|DOI: 10.4018/IJEHMC.2020070101

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MLA

Yadav, Nisha, et al. "A Survey on Fatigue Detection of Workers Using Machine Learning." IJEHMC vol.11, no.3 2020: pp.1-8. http://doi.org/10.4018/IJEHMC.2020070101

APA

Yadav, N., Banerjee, K., & Bali, V. (2020). A Survey on Fatigue Detection of Workers Using Machine Learning. International Journal of E-Health and Medical Communications (IJEHMC), 11(3), 1-8. http://doi.org/10.4018/IJEHMC.2020070101

Chicago

Yadav, Nisha, Kakoli Banerjee, and Vikram Bali. "A Survey on Fatigue Detection of Workers Using Machine Learning," International Journal of E-Health and Medical Communications (IJEHMC) 11, no.3: 1-8. http://doi.org/10.4018/IJEHMC.2020070101

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Abstract

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.